Overview

Dataset statistics

Number of variables11
Number of observations2778702
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory233.2 MiB
Average record size in memory88.0 B

Variable types

Categorical1
DateTime1
Numeric9

Warnings

id_estacion has a high cardinality: 207 distinct values High cardinality
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
longitud is highly correlated with latitudHigh correlation
latitud is highly correlated with longitudHigh correlation
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
tmin is highly correlated with fecha_cnt and 1 other fieldsHigh correlation
latitud is highly correlated with longitud and 1 other fieldsHigh correlation
fecha_cnt is highly correlated with tmin and 1 other fieldsHigh correlation
longitud is highly correlated with latitud and 1 other fieldsHigh correlation
altitud is highly correlated with latitud and 1 other fieldsHigh correlation
tmax is highly correlated with tmin and 1 other fieldsHigh correlation
nevada is highly skewed (γ1 = 589.8913408) Skewed
prof_nieve is highly skewed (γ1 = 65.86614932) Skewed
precip has 2066251 (74.4%) zeros Zeros
nevada has 2778673 (> 99.9%) zeros Zeros
prof_nieve has 2772465 (99.8%) zeros Zeros

Reproduction

Analysis started2021-10-09 13:00:35.267498
Analysis finished2021-10-09 13:01:50.708049
Duration1 minute and 15.44 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id_estacion
Categorical

HIGH CARDINALITY

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.2 MiB
SP000009981
 
42185
SP000008280
 
40383
SP000003195
 
36896
SPE00120629
 
36821
SPE00155259
 
36704
Other values (202)
2585713 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters30565722
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP000003195
2nd rowSP000003195
3rd rowSP000003195
4th rowSP000003195
5th rowSP000003195

Common Values

ValueCountFrequency (%)
SP00000998142185
 
1.5%
SP00000828040383
 
1.5%
SP00000319536896
 
1.3%
SPE0012062936821
 
1.3%
SPE0015525936704
 
1.3%
SP00006001036284
 
1.3%
SP00000802734301
 
1.2%
SPE0012045833080
 
1.2%
SPE0011971133057
 
1.2%
SPE0012062032466
 
1.2%
Other values (197)2416525
87.0%

Length

2021-10-09T13:01:50.889822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp00000998142185
 
1.5%
sp00000828040383
 
1.5%
sp00000319536896
 
1.3%
spe0012062936821
 
1.3%
spe0015525936704
 
1.3%
sp00006001036284
 
1.3%
sp00000802734301
 
1.2%
spe0012045833080
 
1.2%
spe0011971133057
 
1.2%
spe0012062032466
 
1.2%
Other values (197)2416525
87.0%

Most occurring characters

ValueCountFrequency (%)
09332042
30.5%
13867798
12.7%
S2778702
 
9.1%
P2778702
 
9.1%
22339578
 
7.7%
E2249350
 
7.4%
51406537
 
4.6%
91394106
 
4.6%
61023402
 
3.3%
8967933
 
3.2%
Other values (5)2427572
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22706150
74.3%
Uppercase Letter7859572
 
25.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09332042
41.1%
13867798
17.0%
22339578
 
10.3%
51406537
 
6.2%
91394106
 
6.1%
61023402
 
4.5%
8967933
 
4.3%
3919852
 
4.1%
4856691
 
3.8%
7598211
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S2778702
35.4%
P2778702
35.4%
E2249350
28.6%
W36816
 
0.5%
M16002
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common22706150
74.3%
Latin7859572
 
25.7%

Most frequent character per script

Common
ValueCountFrequency (%)
09332042
41.1%
13867798
17.0%
22339578
 
10.3%
51406537
 
6.2%
91394106
 
6.1%
61023402
 
4.5%
8967933
 
4.3%
3919852
 
4.1%
4856691
 
3.8%
7598211
 
2.6%
Latin
ValueCountFrequency (%)
S2778702
35.4%
P2778702
35.4%
E2249350
28.6%
W36816
 
0.5%
M16002
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30565722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09332042
30.5%
13867798
12.7%
S2778702
 
9.1%
P2778702
 
9.1%
22339578
 
7.7%
E2249350
 
7.4%
51406537
 
4.6%
91394106
 
4.6%
61023402
 
3.3%
8967933
 
3.2%
Other values (5)2427572
 
7.9%

fecha
Date

Distinct44554
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size21.2 MiB
Minimum1896-11-01 00:00:00
Maximum2021-08-10 00:00:00
2021-10-09T13:01:50.975312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:51.083423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fecha_cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.4308911
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 MiB
2021-10-09T13:01:51.196363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q193
median184
Q3274
95-th percentile347
Maximum366
Range365
Interquartile range (IQR)181

Descriptive statistics

Standard deviation105.2283277
Coefficient of variation (CV)0.573667429
Kurtosis-1.194650744
Mean183.4308911
Median Absolute Deviation (MAD)91
Skewness-0.0032271497
Sum509699784
Variance11073.00095
MonotonicityNot monotonic
2021-10-09T13:01:51.411095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1267734
 
0.3%
1227732
 
0.3%
957725
 
0.3%
1247723
 
0.3%
967722
 
0.3%
937721
 
0.3%
1237719
 
0.3%
1217716
 
0.3%
1257713
 
0.3%
927709
 
0.3%
Other values (356)2701488
97.2%
ValueCountFrequency (%)
17584
0.3%
27611
0.3%
37614
0.3%
47608
0.3%
57615
0.3%
67610
0.3%
77545
0.3%
87558
0.3%
97546
0.3%
107561
0.3%
ValueCountFrequency (%)
3661979
 
0.1%
3657601
0.3%
3647607
0.3%
3637587
0.3%
3627600
0.3%
3617611
0.3%
3607583
0.3%
3597562
0.3%
3587576
0.3%
3577578
0.3%

tmax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct632
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.1744948
Minimum-196
Maximum472
Zeros1752
Zeros (%)0.1%
Negative14996
Negative (%)0.5%
Memory size21.2 MiB
2021-10-09T13:01:51.517534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-196
5-th percentile74
Q1145
median200
Q3257
95-th percentile330
Maximum472
Range668
Interquartile range (IQR)112

Descriptive statistics

Standard deviation78.57346962
Coefficient of variation (CV)0.3925248804
Kurtosis-0.2814421038
Mean200.1744948
Median Absolute Deviation (MAD)56
Skewness-0.05182643485
Sum556225269
Variance6173.790128
MonotonicityNot monotonic
2021-10-09T13:01:51.622365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20032790
 
1.2%
15030517
 
1.1%
17030282
 
1.1%
18030107
 
1.1%
16029697
 
1.1%
21029465
 
1.1%
22029318
 
1.1%
19028687
 
1.0%
23027378
 
1.0%
25026688
 
1.0%
Other values (622)2483773
89.4%
ValueCountFrequency (%)
-1961
< 0.1%
-1911
< 0.1%
-1832
< 0.1%
-1811
< 0.1%
-1751
< 0.1%
-1741
< 0.1%
-1701
< 0.1%
-1671
< 0.1%
-1611
< 0.1%
-1601
< 0.1%
ValueCountFrequency (%)
4721
 
< 0.1%
4691
 
< 0.1%
4663
< 0.1%
4621
 
< 0.1%
4611
 
< 0.1%
4602
 
< 0.1%
4591
 
< 0.1%
4573
< 0.1%
4565
< 0.1%
4552
 
< 0.1%

tmin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct543
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.76497408
Minimum-300
Maximum332
Zeros17473
Zeros (%)0.6%
Negative217549
Negative (%)7.8%
Memory size21.2 MiB
2021-10-09T13:01:51.727606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-300
5-th percentile-16
Q150
median100
Q3150
95-th percentile205
Maximum332
Range632
Interquartile range (IQR)100

Descriptive statistics

Standard deviation67.83950071
Coefficient of variation (CV)0.68687813
Kurtosis-0.429977061
Mean98.76497408
Median Absolute Deviation (MAD)50
Skewness-0.2155019529
Sum274438431
Variance4602.197856
MonotonicityNot monotonic
2021-10-09T13:01:51.827608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10039078
 
1.4%
15035049
 
1.3%
9034794
 
1.3%
8034575
 
1.2%
12034322
 
1.2%
11033833
 
1.2%
7032829
 
1.2%
14032792
 
1.2%
13032746
 
1.2%
6030502
 
1.1%
Other values (533)2438182
87.7%
ValueCountFrequency (%)
-3001
 
< 0.1%
-2821
 
< 0.1%
-2801
 
< 0.1%
-2521
 
< 0.1%
-2481
 
< 0.1%
-2451
 
< 0.1%
-2403
< 0.1%
-2361
 
< 0.1%
-2321
 
< 0.1%
-2311
 
< 0.1%
ValueCountFrequency (%)
3321
< 0.1%
3221
< 0.1%
3191
< 0.1%
3181
< 0.1%
3142
< 0.1%
3101
< 0.1%
3071
< 0.1%
3062
< 0.1%
3041
< 0.1%
3021
< 0.1%

precip
Real number (ℝ≥0)

ZEROS

Distinct1383
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.43283195
Minimum0
Maximum3600
Zeros2066251
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size21.2 MiB
2021-10-09T13:01:51.930179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile100
Maximum3600
Range3600
Interquartile range (IQR)1

Descriptive statistics

Standard deviation59.10199543
Coefficient of variation (CV)3.596580042
Kurtosis148.4722969
Mean16.43283195
Median Absolute Deviation (MAD)0
Skewness8.536300929
Sum45661943
Variance3493.045863
MonotonicityNot monotonic
2021-10-09T13:01:52.029787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02066251
74.4%
141261
 
1.5%
235913
 
1.3%
327965
 
1.0%
422168
 
0.8%
519847
 
0.7%
1019741
 
0.7%
617836
 
0.6%
815445
 
0.6%
713172
 
0.5%
Other values (1373)499103
 
18.0%
ValueCountFrequency (%)
02066251
74.4%
141261
 
1.5%
235913
 
1.3%
327965
 
1.0%
422168
 
0.8%
519847
 
0.7%
617836
 
0.6%
713172
 
0.5%
815445
 
0.6%
910024
 
0.4%
ValueCountFrequency (%)
36001
< 0.1%
33701
< 0.1%
33611
< 0.1%
33001
< 0.1%
32111
< 0.1%
31981
< 0.1%
31301
< 0.1%
29901
< 0.1%
29661
< 0.1%
28001
< 0.1%

nevada
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0002929425322
Minimum0
Maximum119
Zeros2778673
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size21.2 MiB
2021-10-09T13:01:52.118944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum119
Range119
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1299940683
Coefficient of variation (CV)443.7527979
Kurtosis406874.9611
Mean0.0002929425322
Median Absolute Deviation (MAD)0
Skewness589.8913408
Sum814
Variance0.01689845778
MonotonicityNot monotonic
2021-10-09T13:01:52.202459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
02778673
> 99.9%
35
 
< 0.1%
54
 
< 0.1%
83
 
< 0.1%
512
 
< 0.1%
132
 
< 0.1%
282
 
< 0.1%
1191
 
< 0.1%
151
 
< 0.1%
431
 
< 0.1%
Other values (8)8
 
< 0.1%
ValueCountFrequency (%)
02778673
> 99.9%
35
 
< 0.1%
54
 
< 0.1%
83
 
< 0.1%
132
 
< 0.1%
151
 
< 0.1%
181
 
< 0.1%
231
 
< 0.1%
282
 
< 0.1%
301
 
< 0.1%
ValueCountFrequency (%)
1191
< 0.1%
791
< 0.1%
711
< 0.1%
691
< 0.1%
581
< 0.1%
512
< 0.1%
461
< 0.1%
431
< 0.1%
301
< 0.1%
282
< 0.1%

prof_nieve
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4788465982
Minimum0
Maximum2499
Zeros2772465
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size21.2 MiB
2021-10-09T13:01:52.294431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2499
Range2499
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.30344751
Coefficient of variation (CV)38.22403162
Kurtosis5792.795106
Mean0.4788465982
Median Absolute Deviation (MAD)0
Skewness65.86614932
Sum1330572
Variance335.0161907
MonotonicityNot monotonic
2021-10-09T13:01:52.393449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02772465
99.8%
101228
 
< 0.1%
20537
 
< 0.1%
30330
 
< 0.1%
51318
 
< 0.1%
99264
 
< 0.1%
41259
 
< 0.1%
201197
 
< 0.1%
150187
 
< 0.1%
79153
 
< 0.1%
Other values (133)2764
 
0.1%
ValueCountFrequency (%)
02772465
99.8%
101228
 
< 0.1%
20537
 
< 0.1%
258
 
< 0.1%
30330
 
< 0.1%
41259
 
< 0.1%
51318
 
< 0.1%
61136
 
< 0.1%
71148
 
< 0.1%
762
 
< 0.1%
ValueCountFrequency (%)
249912
< 0.1%
23015
 
< 0.1%
22003
 
< 0.1%
20801
 
< 0.1%
199914
< 0.1%
19005
 
< 0.1%
18016
 
< 0.1%
175017
< 0.1%
17091
 
< 0.1%
169917
< 0.1%

longitud
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.70648301
Minimum27.8189
Maximum43.5667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 MiB
2021-10-09T13:01:52.488234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27.8189
5-th percentile28.4775
Q138.2828
median40.8442
Q342.0831
95-th percentile43.3669
Maximum43.5667
Range15.7478
Interquartile range (IQR)3.8003

Descriptive statistics

Standard deviation3.73806283
Coefficient of variation (CV)0.09414238044
Kurtosis3.245285269
Mean39.70648301
Median Absolute Deviation (MAD)1.5958
Skewness-1.86675048
Sum110332483.8
Variance13.97311372
MonotonicityNot monotonic
2021-10-09T13:01:52.587237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.820642185
 
1.5%
38.951940383
 
1.5%
40.411736896
 
1.3%
41.114436821
 
1.3%
41.418136704
 
1.3%
28.308936284
 
1.3%
38.989236155
 
1.3%
43.307534301
 
1.2%
28.463133080
 
1.2%
43.366933057
 
1.2%
Other values (191)2412836
86.8%
ValueCountFrequency (%)
27.818917333
0.6%
27.922510262
 
0.4%
28.047514855
0.5%
28.308936284
1.3%
28.444419576
0.7%
28.463133080
1.2%
28.477528684
1.0%
28.633119113
0.7%
28.951718366
0.7%
35.277821544
0.8%
ValueCountFrequency (%)
43.566719167
0.7%
43.560612347
 
0.4%
43.538122792
0.8%
43.491716485
0.6%
43.464426669
1.0%
43.429221429
0.8%
43.366933057
1.2%
43.360621511
0.8%
43.354217733
0.6%
43.307534301
1.2%

latitud
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct206
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.417536764
Minimum-17.8889
Maximum4.2156
Zeros0
Zeros (%)0.0%
Negative1933173
Negative (%)69.6%
Memory size21.2 MiB
2021-10-09T13:01:52.689962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-17.8889
5-th percentile-16.2553
Q1-5.6417
median-3.4503
Q30.4914
95-th percentile2.3767
Maximum4.2156
Range22.1045
Interquartile range (IQR)6.1331

Descriptive statistics

Standard deviation4.689366735
Coefficient of variation (CV)-1.372148146
Kurtosis1.542218244
Mean-3.417536764
Median Absolute Deviation (MAD)2.6053
Skewness-1.171888945
Sum-9496316.242
Variance21.99016038
MonotonicityNot monotonic
2021-10-09T13:01:52.785132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.789245659
 
1.6%
0.491442185
 
1.5%
-1.863140383
 
1.5%
-3.678136896
 
1.3%
-1.410636821
 
1.3%
2.123936704
 
1.3%
-16.499236284
 
1.3%
-2.039234301
 
1.2%
-16.255333080
 
1.2%
-8.419233057
 
1.2%
Other values (196)2403332
86.5%
ValueCountFrequency (%)
-17.888917333
0.6%
-17.75519113
0.7%
-16.560614855
0.5%
-16.499236284
1.3%
-16.329228684
1.0%
-16.255333080
1.2%
-15.389210262
 
0.4%
-13.863119576
0.7%
-13.600318366
0.7%
-8.64949706
 
0.3%
ValueCountFrequency (%)
4.215619696
0.7%
3.18174598
 
0.2%
3.16584603
 
0.2%
3.09674604
 
0.2%
3.03534601
 
0.2%
3.03254601
 
0.2%
2.83421681
 
0.1%
2.82674003
 
0.1%
2.825321143
0.8%
2.80673615
 
0.1%

altitud
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.0443864
Minimum1
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.2 MiB
2021-10-09T13:01:52.883323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q142
median251
Q3667
95-th percentile1143
Maximum2535
Range2534
Interquartile range (IQR)625

Descriptive statistics

Standard deviation506.8215407
Coefficient of variation (CV)1.19239674
Kurtosis4.529438433
Mean425.0443864
Median Absolute Deviation (MAD)237
Skewness1.941035627
Sum1181071687
Variance256868.0741
MonotonicityNot monotonic
2021-10-09T13:01:52.984456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
482977
 
3.0%
159206
 
2.1%
3550120
 
1.8%
3248181
 
1.7%
4442185
 
1.5%
6441524
 
1.5%
70440383
 
1.5%
739402
 
1.4%
2538990
 
1.4%
537975
 
1.4%
Other values (163)2297759
82.7%
ValueCountFrequency (%)
159206
2.1%
29206
 
0.3%
322163
 
0.8%
482977
3.0%
537975
1.4%
619208
 
0.7%
739402
1.4%
83427
 
0.1%
1131219
 
1.1%
1422967
 
0.8%
ValueCountFrequency (%)
25354573
 
0.2%
25194565
 
0.2%
24514739
 
0.2%
24004503
 
0.2%
237136284
1.3%
23164593
 
0.2%
22664603
 
0.2%
22474444
 
0.2%
22304601
 
0.2%
22284579
 
0.2%

Interactions

2021-10-09T13:01:11.351519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:11.816140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:12.262885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:12.684916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:13.093493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:13.513503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:13.961984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:14.408899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:14.819111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:15.247291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:15.700212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:16.158337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:16.570611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:16.974814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:17.385921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:17.796771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:18.253481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:18.699894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:19.139950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:19.592921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:20.039208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:20.449659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:20.866780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:21.268341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:21.684602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:22.104260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:22.522501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:22.935761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:23.395009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:23.861123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:24.260597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:24.671804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:25.072587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:25.475472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:25.908122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:26.333009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:26.784146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:27.214178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:27.649792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:28.071401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:28.496524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:28.912589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:29.307671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:29.713495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:30.130389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:30.557374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:31.005750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:31.447717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:31.848135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:32.256051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:32.651216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:33.062966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:33.496132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:33.939903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:34.510380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:34.947152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:35.379113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:35.792457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:36.209863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:36.624639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:37.025703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:37.454506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:37.882478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:38.354181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:38.839058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:39.291713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:39.713297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:40.131380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:40.544719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:40.955516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:41.379678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:41.805081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:42.274679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:42.712522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:43.157934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:43.606004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:44.039340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:44.442583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:44.821806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:45.208521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:01:45.574973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-09T13:01:53.084118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-09T13:01:53.210000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-09T13:01:53.329981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-09T13:01:53.452353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-09T13:01:45.825939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-09T13:01:47.331356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

id_estacionfechafecha_cnttmaxtminprecipnevadaprof_nievelongitudlatitudaltitud
0SP0000031951920-01-011119.088.00.00.00.040.4117-3.6781667.0
1SP0000031951920-01-022110.026.00.00.00.040.4117-3.6781667.0
2SP0000031951920-01-03386.024.014.00.00.040.4117-3.6781667.0
3SP0000031951920-01-04468.025.00.00.00.040.4117-3.6781667.0
4SP0000031951920-01-05566.016.00.00.00.040.4117-3.6781667.0
5SP0000031951920-01-06658.0-15.00.00.00.040.4117-3.6781667.0
6SP0000031951920-01-07769.08.00.00.00.040.4117-3.6781667.0
7SP0000031951920-01-08862.0-16.00.00.00.040.4117-3.6781667.0
8SP0000031951920-01-099122.0-12.00.00.00.040.4117-3.6781667.0
9SP0000031951920-01-101097.09.00.00.00.040.4117-3.6781667.0

Last rows

id_estacionfechafecha_cnttmaxtminprecipnevadaprof_nievelongitudlatitudaltitud
2778692SPW000140111967-12-2235661.0-11.00.00.00.040.4833-3.45608.1
2778693SPW000140111967-12-2335733.00.03.00.00.040.4833-3.45608.1
2778694SPW000140111967-12-2435856.017.010.00.00.040.4833-3.45608.1
2778695SPW000140111967-12-2535989.0-22.00.00.00.040.4833-3.45608.1
2778696SPW000140111967-12-26360100.044.00.00.00.040.4833-3.45608.1
2778697SPW000140111967-12-2736194.00.00.00.00.040.4833-3.45608.1
2778698SPW000140111967-12-2836294.0-28.00.00.00.040.4833-3.45608.1
2778699SPW000140111967-12-2936372.0-33.00.00.00.040.4833-3.45608.1
2778700SPW000140111967-12-3036461.0-33.00.00.00.040.4833-3.45608.1
2778701SPW000140111967-12-3136567.0-61.00.00.00.040.4833-3.45608.1